A topic model approach to measuring interdisciplinarity at the National Science Foundation

As the National Science Foundation (NSF) implements new cross-cutting initiatives and programs, interest in assessing the success of these experiments in fostering interdisciplinarity grows. A primary challenge in measuring interdisciplinarity is identifying and bounding the discrete disciplines that comprise interdisciplinary work. Using statistical text-mining techniques to extract topic bins, the NSF recently developed a topic map of all of their awards issued between 2000 and 2011. These new data provide a novel means for measuring interdisciplinarity by assessing the language or content of award proposals. Using the Directorate for Social, Behavioral, and Economic Sciences as a case study and drawing on the new topic model of the NSF’s awards, this paper explores new methods for quantifying interdisciplinarity in the NSF portfolio.